Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors
Abstract Freezing of gait is a common gait disorder among patients with advanced Parkinson’s disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were plac...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-06-01
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| Series: | BMC Neurology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12883-022-02732-z |
| _version_ | 1818257043736231936 |
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| author | Kang Ren Zhonglue Chen Yun Ling Jin Zhao |
| author_facet | Kang Ren Zhonglue Chen Yun Ling Jin Zhao |
| author_sort | Kang Ren |
| collection | DOAJ |
| description | Abstract Freezing of gait is a common gait disorder among patients with advanced Parkinson’s disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients. On this basis, the research on the optimal feature extraction method, sensor configuration, and feature quantity selection in the FoG detection process is carried out. Thirteen typical features consisting of time domain, frequency domain and statistical features were extracted from the sensor signals. Firstly, we used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, we evaluated the detection effects with different combinations of sensors to get the best sensors configuration. Finally, we selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved. This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment. |
| first_indexed | 2024-12-12T17:37:23Z |
| format | Article |
| id | doaj.art-5fc8657e5c2348f9bc47dc60b6574fa2 |
| institution | Directory Open Access Journal |
| issn | 1471-2377 |
| language | English |
| last_indexed | 2024-12-12T17:37:23Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Neurology |
| spelling | doaj.art-5fc8657e5c2348f9bc47dc60b6574fa22022-12-22T00:17:11ZengBMCBMC Neurology1471-23772022-06-0122111310.1186/s12883-022-02732-zRecognition of freezing of gait in Parkinson’s disease based on combined wearable sensorsKang Ren0Zhonglue Chen1Yun Ling2Jin Zhao3System Informatics, Kobe UniversityGYENNO SCIENCE CO., LTD.GYENNO SCIENCE CO., LTD.Key Laboratory of Image Information Processing and Intelligent Control, Ministry of Education, and the School of Artificial Intelligence and Automation, Huazhong University of Science and TechnologyAbstract Freezing of gait is a common gait disorder among patients with advanced Parkinson’s disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients. On this basis, the research on the optimal feature extraction method, sensor configuration, and feature quantity selection in the FoG detection process is carried out. Thirteen typical features consisting of time domain, frequency domain and statistical features were extracted from the sensor signals. Firstly, we used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, we evaluated the detection effects with different combinations of sensors to get the best sensors configuration. Finally, we selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved. This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.https://doi.org/10.1186/s12883-022-02732-zParkinson’s diseaseFreezing of gaitSensor configurationFeature selection |
| spellingShingle | Kang Ren Zhonglue Chen Yun Ling Jin Zhao Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors BMC Neurology Parkinson’s disease Freezing of gait Sensor configuration Feature selection |
| title | Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors |
| title_full | Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors |
| title_fullStr | Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors |
| title_full_unstemmed | Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors |
| title_short | Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors |
| title_sort | recognition of freezing of gait in parkinson s disease based on combined wearable sensors |
| topic | Parkinson’s disease Freezing of gait Sensor configuration Feature selection |
| url | https://doi.org/10.1186/s12883-022-02732-z |
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